Know Your Score

Six questions. Find out where your data foundation actually stands — and where it's most likely to crack under pressure.

Your overall score and band are free. Unlock the per-pillar breakdown and your highest-leverage next step with your email.

Most data organizations don’t find out which constraint is actually missing until it fails in public — a board deck, a fundraise, an AI rollout. This is a faster way to find out.

Know Your Score

Six questions, two minutes. Answer honestly — the point isn't a perfect score, it's finding out which constraint is actually missing before it fails in public.

1. When the same customer, deal, or portfolio company shows up in more than one system, how does your team know it's the same entity?

We don't reliably know — someone has to check manually.Each system assigns its own ID and someone tries to match them later.There's a shared ID, but it was bolted on after the fact and doesn't always resolve cleanly.One hub resolves identity once, at the moment the record is created, and every system inherits it.

2. How many places in your data estate could plausibly claim to be the source of truth for a given entity?

More than we could count.A handful, and they don't always agree.Two or three, mostly aligned.Exactly one.

3. When bad data enters your systems, where does it usually get caught?

In the boardroom, or after a client or stakeholder notices.During the scramble before a big deliverable ships.Somewhere downstream, by whoever happens to be checking that report.At the point it enters — before it reaches any report.

4. How many people currently have to re-verify a number before your team trusts it?

Three or more, every cycle.One or two, most cycles.Usually just the person who built the report.Nobody — the architecture already caught what needed catching.

5. When someone on your team uses an AI tool to shortcut manual data work, what governs what goes in and what comes out?

Nothing formal — it's on the individual.Informal norms, not enforced by any system.Spot checks after the fact.The same validation that governs everything else — AI inherits it automatically.

6. As your team builds and ships faster, does your data architecture get more consistent or less?

Less — every team ends up with its own definitions.It's a mixed bag depending on the team.Mostly holds, with occasional drift.More — the constraints scale with the speed.

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